AI Equity Self Assessment Checklist for Educators

The Opportunity

Imagine getting the opportunity to influence the US Department of Education’s direction on AI education policy—not just to bridge the digital divide, but to prevent it from widening. This isn’t merely a chance for discussion; it’s a privilege to contribute meaningfully and comprehensively to shaping the future of education.

My goal was to provide the Department with tangible resources to:

    • Facilitate their internal discussions.
    • Evaluate existing policies.
    • Inspire new areas of focus where the digital divide intersects with AI across students’ laptops, services, and programs.

The Stakes

Teachers already have a lasting, massive impact on how students think, solve problems, and respond to challenges. Now, with the inevitable AI integration across the US public education system, we face a new risk: the biases inherent in the data of Large Language Models (LLMs) could influence an entire generation’s mindsets and behaviors.

Consider this:

    • Our children spend more of their waking hours at school with teachers than with parents or guardians.
    • This is especially true for computational and emotional intelligence development.

If we’re not careful, AI could amplify existing biases rather than leveling the playing field. However, this is just one of many evolving issues to consider in a complex system of inequities, knowns, and unknowns.

On the flip side, the opportunities for growth that AI brings, if implemented thoughtfully, could enhance our children’s capacities beyond what we can currently predict. This new generation has the potential to leverage AI in ways that could revolutionize learning and problem-solving, opening doors to unprecedented possibilities and equity.

The Invitation

I was invited to a listening session with the U.S. Department of Education’s Office of Educational Technology. Their goal? To learn more about:

    1. Policy needs
    2. Support for capacity building

My focus was particularly aimed at low-income and digitally underserved communities in US Promise and Opportunity Zones—the very communities my nonprofit, mohuman, works with.

Taking Action

To bridge the gap between community needs and education leadership, I created a comprehensive self-assessment checklist for all educational leaders in the US. While I initially developed this to share with the US Department of Education, I believe in open knowledge sharing and want to disseminate it to everyone who can benefit from it. This AI Equity Self-Assessment Checklist is designed to help education leaders ensure AI serves all students equitably, regardless of their background or circumstances.

I want to share key recommendations from the AI Equity Self-Assessment Checklist to help education leaders ensure AI serves all students equitably. This tool is designed to guide informed, equitable decisions regarding AI integration in education. The checklist can be used to:

    1. Guide internal discussions
    2. Evaluate existing policies and initiatives
    3. Inspire new, equitable approaches to AI in education

Moreover, it’s not just for administrators. Parents, teachers, and students can also use this checklist to:

    • Evaluate current educational practices
    • Comment on proposed AI initiatives
    • Advocate for equitable education policies in their communities

By making this resource widely available, I aim to empower all stakeholders in the education system to contribute to a more equitable AI-integrated future. Download the comprehensive list at the end of this post.

Key Recommendations

1. Equity and Access

Ensure underserved communities have high-speed internet and AI-capable devices. Assess gaps in digital infrastructure and collaborate with local providers.

2. Capacity Building

Provide comprehensive AI training for educators, with ongoing professional development to keep them updated on emerging tools and technologies.

3. Ethical AI Use

Audit AI systems for biases that might affect underrepresented groups, ensuring AI tools are culturally inclusive and fair.

4. Data Privacy and Security

Implement strict guidelines for student data protection, ensuring compliance with relevant privacy laws and robust cybersecurity measures.

5. Diversity in Leadership

Foster diversity in leadership roles, ensuring leaders reflect the communities they serve and understand challenges faced by underserved populations.

6. Community Trust and Engagement

Engage communities, especially those underserved, to build trust in AI technologies. Hold regular meetings and establish feedback mechanisms.

7. Addressing the Digital Divide

Move beyond temporary fixes like hotspots; invest in sustainable, long-term infrastructure improvements for reliable connectivity.

8. Ethical Funding Practices

Ensure funding is distributed transparently and equitably. Simplify application processes to help smaller communities access resources.

9. Long-Term Impact and Evaluation

Establish clear metrics to assess AI’s long-term effects on equity and learning outcomes, with regular evaluations to adjust policies.

10. Safeguarding Against Manipulation

Implement safeguards to prevent manipulation of AI systems and educate students on ethical AI practices and responsible digital citizenship.

 

Download the Full AI Equity Self-Assessment Checklist

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